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Found 8 papers, 1 papers with code
One Masked Model is All You Need for Sensor Fault Detection, Isolation and Accommodation
Our proposed technique has the potential to significantly improve the accuracy and reliability of sensor measurements in complex engineering systems in real-time, and could be applied to other types of sensors and engineering systems in the future.
Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications
This paper begins with a description of methods for estimating image probability density functions that reflects the observation that such data is usually constrained to lie in restricted regions of the high-dimensional image space-not every pattern of pixels is an image.
Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand
Models trainded with MMMPF can also generate desired quantiles to capture uncertainty and enable probabilistic planning for grid of the future.
Masked Multi-Step Multivariate Time Series Forecasting with Future Information
Furthermore, once a neural network model is trained with MMMF, its inference speed is similar to that of the same model trained with traditional regression formulations, thus making MMMF a better alternative to existing regression-trained time series forecasting models if there is some available future information.
Multi-Agent Learning of Numerical Methods for Hyperbolic PDEs with Factored Dec-MDP
Factored decentralized Markov decision process (Dec-MDP) is a framework for modeling sequential decision making problems in multi-agent systems.
MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series
Our experimental results demonstrate that MAD can achieve better anomaly detection rates over traditional NSP approaches when using exactly the same neural network (NN) base models, and can be modified to run as fast as NSP models during test time on the same hardware, thus making it an ideal upgrade for many existing NSP-based NN anomaly detection models.
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning
We introduce Backpropagation Through Time and Space (BPTTS), a method for training a recurrent spatio-temporal neural network, that is used in a homogeneous multi-agent reinforcement learning (MARL) setting to learn numerical methods for hyperbolic conservation laws.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Dynamically Controlled Recurrent Neural Network for Modeling Dynamical Systems
This work proposes a novel neural network architecture, called the Dynamically Controlled Recurrent Neural Network (DCRNN), specifically designed to model dynamical systems that are governed by ordinary differential equations (ODEs).
